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Evaluation of photography using head-mounted display technology (ICAPS) for district Trachoma surveys

Domaine:

healthcare

Type de record:

paper
Créateur:
FahChrMerMic
Éditeur:
Pub
Hôte:
Background As the prevalence of trachoma declines worldwide, it is becoming increasingly expensive and challenging to standardize graders in the field for surveys to document elimination. Photography of the tarsal conjunctiva and remote interpretation may help alleviate these challenges. The purpose of this study was to develop, and field test an Image Capture and Processing System (ICAPS) to acquire hands-free images of the tarsal conjunctiva for upload to a virtual reading center for remote grading. Methodology/Principal findings This observational study was conducted during a district-level prevalence survey for trachomatous inflammation—follicular (TF) in Chamwino, Tanzania. The ICAPS was developed using a Samsung Galaxy S8 smartphone, a Samsung Gear VR headset, a foot pedal trigger and customized software allowing for hands-free photography. After a one-day training course, three trachoma graders used the ICAPS to collect images from 1305 children ages 1–9 years, which were expert-graded remotely for comparison with field grades. In our experience, the ICAPS was successful at scanning and assigning barcodes to images, focusing on the everted eyelid with adequate examiner hand visualization, and capturing images with sufficient detail to grade TF. The percentage of children with TF by photos and by field grade was 5%. Agreement between grading of the images compared to the field grades at the child level was kappa = 0.53 (95%CI = 0.40–0.66). There were ungradable images for at least one eye in 199 children (9.1%), with more occurring in children ages 1–3 (18.5%) than older children ages 4–9 (4.2%) (χ 2 = 145.3, p <0.001). Conclusions/Significance The prototype ICAPS device was robust, able to image 1305 children in a district level survey and transmit images from rural Tanzania to an online grading platform. More work is needed to improve the percentage of ungradable images and to better understand the causes of disagreement between field and photo grading.

Visit

doi.org

Tasks

computer visionimage classification

Licenses

http://creativecommons.org/licenses/by/4.0/

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